Li Chang, Hui Dongming, Wu Faqi, Xia Yuwei, Shi Feng, Yang Mingguang, Zhang Jinrui, Peng Chao, Feng Junbang, Li Chuanming
Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
Department of Radiology, Chongqing Western Hospital, Chongqing, China.
Front Med (Lausanne). 2024 Jan 5;10:1303501. doi: 10.3389/fmed.2023.1303501. eCollection 2023.
Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).
155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.
Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.
The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.
帕金森病（PD）是第二常见的神经退行性疾病。临床实践中迫切需要一种客观的诊断方法。在本研究中，对深度学习和放射组学技术进行了研究，以从健康对照（HCs）中自动诊断PD。
将155例PD患者和154例HCs随机分为训练集（246例患者）和测试集（63例患者）。使用VB-net自动进行脑区识别和分割，并提取双侧丘脑、尾状核、壳核和苍白球的放射组学特征。在训练集上训练五个独立的机器学习分类器[支持向量机（SVM）、随机梯度下降（SGD）、随机森林（RF）、二次判别分析（QDA）和决策树（DT）]，并在测试集上进行验证。使用德龙检验比较不同模型的性能。
我们的VB-net可以自动将脑部分割为109个区域。从每位患者的双侧丘脑、尾状核、壳核或苍白球中自动提取2264个放射组学特征。经过四步特征降维后，德龙检验表明基于组合特征的SVM模型性能最佳，在训练集和测试集中的曲线下面积（AUC）分别为0.988（95%CI：0.979～0.998，特异性=91.1%，敏感性=100%，准确性=89.4%，精确性=88.2%）和0.976（95%CI：0.942～1.000，特异性=100%，敏感性=87.1%，准确性=93.5%，精确性=88.6%）。决策曲线分析表明折线图模型的临床获益较高。
基于组合特征的SVM模型可用于高精度诊断PD。我们的全自动模型可以在一分钟内快速处理MRI数据并区分PD患者和HCs。它大大提高了诊断效率，在临床实践中对帮助PD的早期诊断具有很大的潜在价值。
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